# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"

# Select UCEC cohort as it's related to endometrial conditions
selected_cohort = "TCGA_Endometrioid_Cancer_(UCEC)"
cohort_dir = os.path.join(tcga_root_dir, selected_cohort)

# Get file paths for clinical and genetic data
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

# Check directory and files
import os

cohort_dir = os.path.join(tcga_root_dir, "TCGA_Endometrioid_Cancer_(UCEC)")

# Read the clinical data file 
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Extract and preview candidate age columns
age_preview = {}
if candidate_age_cols:
    age_data = clinical_df[candidate_age_cols]
    age_preview = preview_df(age_data)
    print("\nAge column preview:")
    print(age_preview)

# Extract and preview candidate gender columns
gender_preview = {}
if candidate_gender_cols:
    gender_data = clinical_df[candidate_gender_cols]
    gender_preview = preview_df(gender_data)
    print("\nGender column preview:")
    print(gender_preview)
candidate_age_cols = ["_AGE", "AGE", "age", "Age", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["_GENDER", "GENDER", "gender", "Gender", "SEX", "sex", "Sex"]

# Since we have defined candidate columns but don't have clinical data to preview yet,
# keep empty placeholders for preview variables
age_preview = {} 
gender_preview = {}
# Since we need the candidate columns and their preview values from the previous step,
# we should raise an error to indicate missing required input
raise ValueError("Missing required input: Need candidate demographic columns and their preview values from the previous step to make informed column selection.")
# Select UCEC cohort as it's related to endometrial conditions
selected_cohort = "TCGA_Endometrioid_Cancer_(UCEC)"
cohort_dir = os.path.join(tcga_root_dir, selected_cohort)

# Get file paths for clinical and genetic data
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

# Get data files directly from root directory 
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_dir)

# Read clinical data
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Extract and preview age columns if any exist
if candidate_age_cols:
    age_preview = clinical_df[candidate_age_cols].head().to_dict('list')
    print("Age columns preview:", age_preview)

# Extract and preview gender columns if any exist  
if candidate_gender_cols:
    gender_preview = clinical_df[candidate_gender_cols].head().to_dict('list')
    print("Gender columns preview:", gender_preview)
# Select UCEC cohort as it's related to endometrial conditions
selected_cohort = "TCGA_Endometrioid_Cancer_(UCEC)"
cohort_dir = os.path.join(tcga_root_dir, selected_cohort)

# Get file paths for clinical and genetic data
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get correct file paths using helper function
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_dir)

# Read clinical data
clinical_data = pd.read_csv(clinical_file_path, index_col=0, sep='\t')

# Extract candidate columns
age_data = clinical_data[candidate_age_cols]
gender_data = clinical_data[candidate_gender_cols]

# Preview data 
print("Age columns preview:")
print(preview_df(age_data))
print("\nGender columns preview:")
print(preview_df(gender_data))
# Select UCEC cohort as it's related to endometrial conditions
selected_cohort = "TCGA_Endometrioid_Cancer_(UCEC)"
cohort_dir = os.path.join(tcga_root_dir, selected_cohort)

# Get file paths for clinical and genetic data
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Identify candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

# Load the clinical data file directly from tcga_root_dir
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Preview age columns
if candidate_age_cols:
    age_preview = clinical_df[candidate_age_cols].head()
    print("Age columns preview:")
    print(preview_df(age_preview))

# Preview gender columns  
if candidate_gender_cols:
    gender_preview = clinical_df[candidate_gender_cols].head() 
    print("\nGender columns preview:")
    print(preview_df(gender_preview))
# Set default values for demographic columns
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# 1. Extract and standardize clinical features
# Create trait labels from sample IDs (01-09: tumor=1, 10-19: normal=0)
clinical_features = tcga_select_clinical_features(
    clinical_df, 
    trait=trait,
    age_col='age_at_initial_pathologic_diagnosis',
    gender_col='gender'
)
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)

# 2. Normalize gene symbols and save
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

# 3. Link clinical and genetic data on sample IDs
linked_data = pd.merge(
    clinical_features, 
    normalized_gene_df.T,
    left_index=True,
    right_index=True,
    how='inner'
)

# 4. Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

# 5. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate data quality and save cohort info
note = "Contains molecular data from tumor and normal samples with patient demographics."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 7. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)